5 Major Challenges In Building AI MVP In 2025

In the last few years, companies of all sizes have increasingly applied Artificial Intelligence (AI) to their business models to maintain a competitive edge in the intense marketplace. As a result, developing an AI Minimum Viable Product (MVP) has become a strategic approach to validate concepts and accelerate time-to-market. 

However, deploying an AI MVP from scratch is not simple, and many potential obstacles are ahead. To help you deal with such issues, this article will introduce you to the top five challenges encountered when building an AI MVP in 2025, providing insights and strategies for effectively navigating these complexities.

Top 5 Main Challenges in Building AI MVP

Since there are lots of obstacles and difficulties in building the AI MVP, I will only focus on the five main challenges that can significantly impact your business:

1. Low-Quality And Insufficient Data

The effectiveness of AI models heavily relies on high-quality, relevant datasets. Poor data quality, characterized by inaccuracies or biases, can lead to unreliable AI outcomes. For instance, an AI system trained on biased data may perpetuate those biases, resulting in unfair or incorrect predictions. 

A real-world example is Apple’s attempt to integrate Baidu’s AI models into iPhones sold in China, where challenges in understanding and responding accurately to large language models posed significant obstacles, potentially impacting product performance in that market.

Solutions:

• Invest in Data Quality Tools: Utilize tools that automate data validation and cleansing processes.
• Collaborate with Data Providers: Partner with reliable data vendors to access high-quality datasets relevant to your AI MVP.​
• Implement Data Governance: Establish precise data sourcing, cleaning, and maintenance protocols to ensure data integrity.​

Implement Data Governance

2. Unclear Strategy And Undefined Scope

Without a well-defined strategy and clear objectives, AI projects risk misalignment with business goals, leading to wasted resources and suboptimal products. This lack of direction can result in AI solutions that do not address actual business needs and fail to deliver value. 

For example, many CEOs have expressed anxiety about their AI strategies, fearing job loss within two years if they fail to achieve meaningful AI-driven results, highlighting the critical need for a clear and effective AI strategy.

Solutions:

• Define Clear Objectives: Align AI MVP goals with overall business objectives to ensure relevance and value.​
• Conduct Market Research: Understand market needs and tailor the AI MVP to address specific pain points.​
• Set Measurable KPIs: Establish key performance indicators to track progress and success.

3. Incompatible Legacy Systems

Seamlessly incorporating AI solutions into current infrastructures is complex. Compatibility issues can cause operational disruptions, leading to inefficiencies and increased costs. Apple’s collaboration with Baidu to integrate AI models into iPhones in China faced significant challenges, particularly in understanding and response accuracy, potentially impacting product performance and sales in that market.

Incompatible Legacy Systems

Solutions:

• Conduct System Audits: Assess existing infrastructure to identify integration challenges.​
• Use Middleware Solutions: Employ middleware to facilitate communication between new AI components and legacy systems.​
• Plan for Incremental Integration: Gradually integrate AI solutions to minimize disruptions and allow for troubleshooting.

4. Significant Talent Shortages

The specialized nature of AI development requires skilled professionals. A shortage of such talent can hinder project progress and innovation. For instance, many Australian companies struggle to transition AI projects from pilot phases to full production, missing out on AI’s potential benefits due to a lack of skilled personnel to effectively implement and scale these technologies.

Solutions:

• Invest in Training Programs: Provide current employees opportunities to learn AI-related skills.​
• Partner with Educational Institutions: Collaborate with universities and training centers to cultivate a pipeline of AI talent.​
• Leverage AI-as-a-Service Platforms: Utilize platforms offering AI capabilities without in-house expertise.

5. High Costs And Limited Scalability

AI projects often demand substantial financial investments; scaling these solutions can be resource-intensive. Without careful planning, costs can escalate, making projects financially unviable. 

For example, companies are cautious about adopting AI due to its resource-intensive nature and the risk of investing in non-beneficial use cases, emphasizing the need for AI to deliver clear returns on investment before widespread adoption.

Solutions:

• Start with Pilot Projects: Begin with small-scale AI projects to demonstrate value before scaling up.​
• Seek Funding Opportunities: Explore grants, partnerships, and venture capital to support AI initiatives.
• Adopt Cloud-Based AI Services: Utilize cloud platforms to access AI tools without significant upfront infrastructure costs.​

Adopt Cloud-Based AI Services

Those are the five main challenges you may face during the AI MVP building process. Make sure you can identify the actual issue and match the appropriate solutions.

What Are The Consequences If You Fail To Build The AI MVP?

If you fail to overcome the above challenges, you may likely face several consequences, such as:​

1. Misaligned With User Needs:

• Cause: Assuming AI can address every issue without considering its limitations.​
• Impact: These conditions can result in solutions that are either overly complex or misaligned with user needs, leading to reduced user satisfaction and potential product failure.

2. Market Underperformance and Lack of Competitive Edge:

• Cause: Insufficient long-term business planning and failure to align the product concept with customer needs and market trends.
• Impact: This oversight can hinder market competitiveness, impede effective monetization, and fail to engage the intended user base, ultimately leading to the product’s underperformance.

3. User Dissatisfaction and Reputation Damage Due to Technical Issues:

• Cause: Releasing an MVP with significant technical difficulties due to inadequate testing.​
• Impact: A buggy product can drive users away, damage the company’s reputation, and result in financial losses due to decreased user retention.

4. Increased Costs and Project Delays Due to Low-Quality Development:

• Cause: Choosing a development vendor based solely on low rates without assessing their experience and capability.​
• Impact: Such an outcome can lead to a product that doesn’t meet quality standards, contains numerous bugs, and lacks scalability, ultimately causing project delays and increased costs. 

Overall, businesses need to have some clear strategies for building an AI MVP, depending on the scale and requirements of each company.

Case Studies Of Popular Brands When Building An AI MVP

To give you a closer look at how major brands apply AI to their MVP-building process and what challenges they face, I will present some real-life examples below:

1. Quibi 

Launched in 2018, Quibi was a short-form streaming service that aimed to revolutionize mobile video consumption. Despite raising $1.75 billion and securing A-list talent, Quibi failed to gain traction and ceased operations by December 2020. Contributing factors to its failure included a misalignment between its AI-driven personalization features and user preferences, leading to low engagement and rapid subscriber loss.

Quibi was a short-form streaming service

Lessons Learned:

• Understand Market Needs: Ensure AI personalization aligns with user preferences to enhance engagement.​
• Flexibility and Adaptation: Push strategies based on user feedback and market trends.​
• Comprehensive Testing: Thoroughly test AI features to validate their effectiveness before full-scale deployment.​

2. BeeQuick 

The BeeQuick transportation app aims to optimize delivery services using AI for real-time routing and scheduling. However, its algorithms failed to manage dynamic routing, resulting in delays and customer dissatisfaction. The inability to meet user expectations led to a swift decline in adoption and eventual shutdown.​

BeeQuick transportation app aims to optimize delivery services

Lessons Learned:

• Robust Algorithm Development: Invest in developing AI algorithms capable of handling real-time data and dynamic conditions.​
• User-Centric Design: Align AI functionalities with user needs to ensure practical benefits and satisfaction.​
• Continuous Improvement: Implement feedback loops to refine AI systems based on real-world performance and user input.

3. HealthSync

HealthSync was a digital health platform that leveraged AI to provide personalized fitness and nutrition recommendations. Launched in 2019 with significant funding, the platform aimed to revolutionize personal health tracking. However, its AI-driven insights often generated inaccurate or irrelevant recommendations, leading to frustrated users who lost trust in the product. Additionally, a lack of real-time adaptability caused users to abandon the platform, ultimately leading to its shutdown in 2022.

Lessons Learned:

• Accurate AI Training: Ensure AI models are trained on diverse, high-quality datasets to improve prediction accuracy.
• User Trust and Transparency: Clearly communicate how AI-generated insights are formed to build user confidence.
• Scalability and Adaptability: Continuously refine AI capabilities to adapt to evolving user needs and real-world data.

These cases underscore the importance of aligning AI capabilities with user expectations, thorough testing, and the agility to adapt strategies based on feedback.

Overcome The Challenges With DEHA Global’s AI MVP Development Service

​DEHA Global offers specialized AI MVP Development Services designed to transform innovative ideas into market-ready products by leveraging Artificial Intelligence (AI) technology. An AI MVP, or Minimum Viable Product, focuses on developing a streamlined version of an AI-driven product with essential functionalities, allowing startups to test feasibility, gather user feedback, and penetrate the market effectively. 

DEHA Global provides a comprehensive suite of services to support businesses in their AI MVP development journey:​

• Idea Analysis and Consultation: They assist businesses in visualizing and assessing the feasibility of AI concepts based on market demand, existing technologies, potential risks, and required resources.
• AI MVP Development: Leveraging Microsoft Azure technologies and a team of skilled engineers, DEHA Global builds AI models, processes data, and develops core features with user-friendly interfaces, ensuring rapid delivery and high performance.
• Testing and Optimization: Beyond development, they support startups by deploying MVPs in real-world scenarios, collecting user feedback, and optimizing products for better performance and stability.
• Fundraising Support: For partners who have collaborated through the development and optimization phases, DEHA Global assists in preparing technical documentation and pitches to present standout product features to investors effectively.

DEHA Global’s AI MVP Development Service

By offering these tailored services, DEHA Global addresses common challenges in building AI MVPs, such as financial constraints, technological hurdles, and infrastructure support. Their expertise enables startups to develop initial product versions cost-effectively and swiftly, facilitating quick market testing and validation. 

This strategic approach enhances product appeal to customers and investors and ensures alignment with market needs and expectations.

Conclusion

This blog explored the top five challenges in building an AI Minimum Viable Product (MVP) in 2025, including data quality issues, unclear strategies, integration difficulties, talent shortages, and cost constraints. Developing a clear strategy tailored to each business’s specific scale and requirements is essential to navigate these challenges successfully.

Remember to read our blogs regularly to update the latest knowledge and tips for building AI MVPs effectively.